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Articles | October 01 2019

Gender Differences in Earnings of Early- and Midcareer Pediatricians

Mary Pat Frintner, MSPH;

aSection of Research, American Academy of Pediatrics, Itasca, Illinois;

Address correspondence to Mary Pat Frintner, MSPH, American University of Pediatrics, 345 Park Blvd, Itasca, IL 60143. Electronic mail: mfrintner@aap.org

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Blake Sisk, PhD;

aDepartment of Research, American Academy of Pediatrics, Itasca, Illinois;

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Bobbi J. Byrne, Doc;

bSection of Neonatal-Perinatal Medicine, Department of Pediatrics, School of Medicine, Indiana Academy, Indianapolis, Indiana;

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Gary L. Freed, MD, MPH;

cDepartment of Pediatrics, University of Michigan, Ann Arbor, Michigan; and

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Amy J. Starmer, MD, MPH;

dDepartment of Medicine, Boston Children'south Hospital, Boston, Massachusetts

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Lynn Chiliad. Olson, PhD

aDepartment of Research, American Academy of Pediatrics, Itasca, Illinois;

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Address correspondence to Mary Pat Frintner, MSPH, American Academy of Pediatrics, 345 Park Blvd, Itasca, IL 60143. E-mail: mfrintner@aap.org

POTENTIAL Conflict OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors take indicated they have no fiscal relationships relevant to this article to disembalm.

Pediatrics (2019) 144 (4): e20183955.

BACKGROUND:

The US physician workforce includes an increasing number of women, with pediatrics having the highest per centum. In recent enquiry on physicians, it is indicated that men earn more than women. It is unclear how this finding extends to pediatricians.

METHODS:

We examined cross-exclusive 2016 information on earnings from the American Academy of Pediatrics Pediatrician Life and Career Experience Study, a longitudinal study of early- and midcareer pediatricians. To approximate adjusted differences in pediatrician earnings betwixt men and women, we conducted 4 ordinary to the lowest degree squares regression models. Model i examined gender, unadjusted; model ii controlled for labor force characteristics; model 3 controlled for both labor force and physician-specific job characteristics; and model 4 controlled for labor force, dr.-specific job, and work-family characteristics.

RESULTS:

60-seven percent of Pediatrician Life and Career Experience Study participants completed the 2016 surveys (1213 out of 1801). The analytic sample was restricted to participants who completed training and worked in general pediatrics, hospitalist care, or subspecialty care (northward = 998). Overall pediatrician-reported mean almanac income was $189 804. Before any adjustment, women earned ∼76% of what men earned, or ∼$51 000 less. Adjusting for common labor force characteristics such as demographics, work hours, and specialty, women earned ∼87% of what men earned, or ∼$26 000 less. Adjusting for a comprehensive gear up of labor force, doctor-specific task, and work-family characteristics, women earned ∼94% of what men earned, or ∼$8000 less.

CONCLUSIONS:

Early- to midcareer female pediatricians earned less than male person pediatricians. This difference persisted subsequently adjustment for important labor forcefulness, physician-specific job, and work-family characteristics. In future work, researchers should utilise longitudinal analyses and further explore family obligations and choices.

What'south Known on This Subject:

In inquiry amid physicians, it is indicated that women earn less than men, even when adjusting for personal and professional characteristics. General pediatricians are frequently grouped with pediatric subspecialists, and there has not been a recent national study in which researchers accept focused solely on pediatrician earnings.

What This Study Adds:

Female pediatricians report earning less than male pediatricians. Before any adjustment, women earn 76% of what men earn, or ∼$51 000 less. Adjusting for a comprehensive gear up of characteristics, women report earning ∼94% of men'south earnings, or ∼$8000 less.

The Us md workforce includes an increasing number of women,ⁱ who bring substantial value to health care.² From 2008 to 2018, women accounted for approximately one-half of medical school graduates.³ In 2017, at least half of physicians in several specialties were women, including child and adolescent psychiatry, geriatric medicine, internal medicine-pediatrics, obstetrics and gynecology, and pediatrics.⁴ Pediatrics includes the highest percentage of women, with 6 in 10 practicing pediatricians^4,5 and 7 in 10 graduating pediatric residents^{half dozen,vii} who are women. Despite the growth of women in pediatrics and similarities in how men and women practise pediatrics,⁸ it is unclear whether they are paid equally.

In many US studies, researchers accept examined income by gender. Specific to physician earnings, some researchers have used data from national surveys (eg, American Community Survey) and report gaps of $fourscore 000 or more than, with no adjustments for physician characteristics.^9,x Gender gaps narrow when adjusting for md characteristics, such as historic period and piece of work hours. Nevertheless, studies based on national surveys are limited because they measure few characteristics important to understanding physician jobs, such as the type of specialty or exercise.¹¹

Other surveys conducted specifically among physicians also written report gender gaps. For example, in a study conducted across specialties (eg, primary intendance, including pediatrics; obstetrics and gynecology; medical; and surgical) in xxx practices and 6 states, male physicians reported earning $ninety 000 more than female person physicians in 2013.¹² After aligning for physician and job characteristics, the gap narrowed substantially, but male earnings remained $27 000 higher. In studies specifically conducted among pediatricians in the 1990s and early 2000s, it was found that female gender was associated with lower annual earnings among pediatricians in office-based practices^thirteen and among chief intendance physicians, including pediatricians across 3 time periods.¹⁴

Limitations in existing studies on doc earnings include single institution settings,¹⁵ academic-specific focus,^15–19 information collected over a decade ago,^13,fourteen and no or limited adjustments to reported earnings.^9,twenty It is suggested in the inquiry that in addition to gender, studies of doctor earnings should account for specialty, work hours, years spent on the job, fourth dimension out of work, other practice-specific characteristics, and geographic location.⁹ Work-life residuum issues and choices made in careers have been less studied, specially among physicians.

A 2017 position article of the American College of Physicians states that more than research on md bounty to examine gender disparities is needed in all practice settings.² Express data sources exist that allow researchers to comprehensively examine the full continuum of labor strength characteristics, physician-specific job characteristics, and life factors that might modify medico wages.

In the broader literature on earnings, it is shown that female person-dominated professions tend to have lower earnings than male-dominated ones.²¹ Consequent with this, in studies on income by specialty, it is suggested that pediatricians are amongst the lowest paid.^17,22 Still, principal care pediatricians are oft grouped with pediatric subspecialists, and to our knowledge, at that place has not been a contempo national study focused solely on pediatrician earnings that includes pediatricians practicing primary, hospital, and subspecialty care. Pediatrics provides a unique opportunity to accept a more in-depth look at the gender earnings gap within an individual specialty because it has the highest proportion of female physicians^iv,23 and most female early- to midcareer pediatricians are mothers who are juggling work and abode responsibilities.²⁴

The American Academy of Pediatrics (AAP) Pediatrician Life and Career Experience Study (PLACES)²⁵ provides a rich source of data that includes domains other studies on doctor earnings have not included. PLACES is an ongoing longitudinal study in which researchers survey pediatricians across their careers on cadre issues, such equally work-life balance, work environment, career satisfaction, daily stressors, life changes, and financial characteristics.²⁶

In the current report, we examine cross-sectional PLACES data from early- to midcareer pediatricians on earnings by gender and explore the extent to which measured labor force (ie, key variables in the US Census Current Population Survey that are used to examine income), md-specific chore, and piece of work-family characteristics account for observed gender differences.

Methods

Data Source and Respondents

We analyzed cantankerous-sectional survey data collected in 2016 from PLACES.²⁶ Participants are surveyed twice each year: a longer survey covering several domains (eg, work characteristics, satisfaction, work-life balance, life experiences) and a shorter survey with more in-depth questions virtually a single topic, such as earnings, which was selected past participants for the 2016 survey.

Pediatricians were recruited in 2012 to participate in PLACES. Participants were randomly selected from an AAP database that includes all pediatricians who completed United states of america residency programs, both AAP members and nonmembers. In recruitment, ii cohorts were targeted: those who completed residency in 2009–2011 and those who completed residency in 2002–2004. Four in x pediatricians (1925 out of 4677) invited to participate in PLACES initially signed up for the longitudinal written report; of these, 1804 (93.7%) completed the first survey and were thereafter considered PLACES participants. Written report participants are more likely to be female person, AAP members, and graduates of US medical schools than target samples of pediatricians; item has been described previously.²⁶

The AAP Institutional Review Board approved the study.

Measures

We linked data from 2 PLACES surveys conducted in 2016 and demographic information collected during study recruitment in 2012. Questions were developed on the basis of review of other studies and input from the AAP PLACES advisory committee.

Participants answered a question on their annual income adjusted from other inquiry^v,27 ("What is your estimated almanac income from your professional activities before taxes? For employees, delight include salary, bonus, and turn a profit sharing contributions. For owners, please include earnings after tax-deductible business organization expenses but before income tax."). Participants likewise answered questions most their demographic, job, and work-family characteristics, many of which were adapted from previous enquiry.^5,7,28–32 We hypothesized that these characteristics would be associated with earnings.

For assay, we grouped the measured variables into 3 key groups: labor force, physician-specific job, and piece of work-family characteristics, which are described in more detail below and in Tabular array one, with further information in Supplemental Table 3.

Table 1

Pediatrician Earnings: Description of Variables That Were Examined

Labor Forcefulness	Physician-Specific Job	Work-Family
Gender	Years in electric current position (continuous variable)	Marital condition
∙ Female	No. patients seen in a typical week (continuous variable)	∙ Yes (married or partnered)
∙ Male person	Owner	∙ No (never married or partnered, divorced, widowed)
Race and ethnicity	∙ No	Parental status: no. of ain children (continuous variable)
Years since residency graduation (continuous variable)	∙ Yep	Choices in income for own children
∙ White, not-Hispanic	Work surface area	∙ No (reported making fiddling or no sacrifice in income for children)
∙ Asian	∙ Suburban	∙ Aye (reported the degree of cede was very much or a fair amount)
∙ Other, including minority (African American or black, Hispanic, American Indian)	∙ Inner city	Choices in income for work environment
Medical school location	∙ Urban	∙ No (reported making picayune or no sacrifice in income for work surround)
∙ US	∙ Rural	∙ Yes (reported the degree of sacrifice was very much or a off-white amount)
∙ International	Bookish setting	Piece of work reduction
Region of land	∙ No	∙ No (always reported working full-fourth dimension)
∙ Northeast	∙ Yes	∙ Yeah (reported working role-time and/or not working on 1 or more than surveys over the last 5 years)
∙ Midwest	Compensation method
∙ South	∙ Mix salary and share of billings or other measures of operation
∙ Due west	∙ Other, including fixed salary, share of exercise billings or workload, and shift or hourly
No. work h per wk (continuous variable)
Specialty
∙ General pediatrics
∙ Hospitalist
∙ Subspecialty, larger: neonatology, pediatric cardiology, pediatric critical intendance, pediatric emergency medicine, pediatric gastroenterology, and pediatric hematology-oncology33
∙ Subspecialty, smaller: all other subspecialties

Labor Strength	Physician-Specific Chore	Work-Family
Gender	Years in current position (continuous variable)	Marital status
∙ Female person	No. patients seen in a typical week (continuous variable)	∙ Yes (married or partnered)
∙ Male	Owner	∙ No (never married or partnered, divorced, widowed)
Race and ethnicity	∙ No	Parental condition: no. of ain children (continuous variable)
Years since residency graduation (continuous variable)	∙ Yep	Choices in income for ain children
∙ White, not-Hispanic	Work area	∙ No (reported making little or no sacrifice in income for children)
∙ Asian	∙ Suburban	∙ Yeah (reported the degree of cede was very much or a fair corporeality)
∙ Other, including minority (African American or blackness, Hispanic, American Indian)	∙ Inner city	Choices in income for work environment
Medical school location	∙ Urban	∙ No (reported making lilliputian or no cede in income for work surround)
∙ U.s.a.	∙ Rural	∙ Yes (reported the caste of sacrifice was very much or a fair amount)
∙ International	Academic setting	Work reduction
Region of country	∙ No	∙ No (e'er reported working full-time)
∙ Northeast	∙ Yes	∙ Yes (reported working function-fourth dimension and/or not working on ane or more surveys over the last 5 years)
∙ Midwest	Compensation method
∙ South	∙ Mix bacon and share of billings or other measures of performance
∙ West	∙ Other, including fixed bacon, share of practice billings or workload, and shift or hourly
No. piece of work h per wk (continuous variable)
Specialty
∙ General pediatrics
∙ Hospitalist
∙ Subspecialty, larger: neonatology, pediatric cardiology, pediatric critical care, pediatric emergency medicine, pediatric gastroenterology, and pediatric hematology-oncology33
∙ Subspecialty, smaller: all other subspecialties

For labor force characteristics, we included gender, years since residency training completion (proxy for age and piece of work feel), race and ethnicity, medical schoolhouse location, region of country, hours worked per week, and primary task or specialty considering these are fundamental variables in the US Census Current Population Survey used to examine income^eleven,34,35 and important conventional human majuscule characteristics.^36,37 For race and ethnicity, because of modest numbers, nosotros combined Hispanic, African American or black, American Indian, and other race into one group. We included region of land because earnings and cost of living might vary across regions³⁸ and hours worked considering function-fourth dimension hours are common among female pediatricians,³⁹ and human capital models betoken piece of work hours are a major source of gender earning gaps.⁴⁰ Specialty (eg, general pediatrics, hospitalist, smaller subspecialty, and larger subspecialty, which is described in detail in Supplemental Table three) was included as a labor strength characteristic because information technology farther defines how occupation and earnings amongst different pediatric subspecialties might vary.⁴¹ We categorized subspecialty as larger (neonatology, cardiology, critical care, emergency medicine, gastroenterology, and hematology-oncology) and smaller (all other subspecialties) on the basis of fellowship size reported past the American Lath of Pediatrics.³³

For physician-specific task characteristics, we included variables that provided more particular on the nature of the pediatrician'southward current job, including number of years in current job, workload (number of patients seen per week), exercise ownership, piece of work setting (area, bookish), and compensation method. In previous inquiry, authors controlled for work load,^xiii practise buying,^13,42,43 work expanse,^13,44 and academic setting⁴⁵ in studies on earnings.

For work-family characteristics, nosotros included marital status, number of children, choices made in job for their ain children, choices made in task for their work surround, and periods of piece of work reduction (reported working role-time and/or non working on i or more than surveys over the final 5 years). These variables provide information oftentimes missing in analyses of income disparities: choices fabricated for their own families (further described in Supplemental Tabular array 3). For example, women may be seeking flexible work schedules and/or take on more responsibilities at habitation.⁴⁶

Data Analysis

Annual earnings self-reported by pediatricians in 2016 were analyzed in several means. First, we examined the distribution and overall hateful of earnings by gender. Second, for the overall sample and separately for women and men, we used t exam and 1-manner analysis of variance to examine pediatrician earnings by each labor strength, physician-specific job, and piece of work-family characteristic. Third, to estimate the adapted divergence in the log of pediatrician earnings between men and women, we used ordinary least squares regression. We conducted 4 regression models. Model 1 examined gender, unadjusted; model two controlled for labor forcefulness characteristics; model 3 controlled for both labor forcefulness and physician-specific job characteristics; and model 4 controlled for labor force, physician-specific job, and piece of work-family characteristics (all characteristics are described in Table 1, Supplemental Tabular array three). We converted the logged regression gender coefficient back to the original scale to help interpret the findings and written report the geometric ways. Nosotros as well used the likelihood ratio exam to compare the model estimates for model 1 and model 2, model 2 and model iii, and model 3 and model 4.

For bivariate tests, continuous variables (years since residency graduation, piece of work hours in a typical week, years in current position, number of patients seen in a typical week, number of children) were categorized for presentation purposes. For multivariable tests, these variables were included as continuous variables, centered at their means.

The number of cases in each analysis varied slightly because of missing values for specific questions. All analyses were conducted by using Stata Version 15 (Stata Corp, Higher Station, TX).

Results

Sixty-seven percentage of PLACES participants completed both 2016 surveys (1213 out of 1801 = 67%), which was similar for women and men (P = .99). The analytic sample was restricted to participants who completed the question on income and worked in full general pediatrics (n = 507), hospitalist care (northward = 118), or subspecialty care (n = 373) in 2016 (total n = 998). The 215 participants who were excluded from assay were trained in a surgical specialty or internal medicine-pediatrics, working in a nonclinical and/or non-US position, in a fellowship training position, not working in 2016, and/or had missing data on income and/or specialty. Overall, 73.5% of the analytic sample are women.

Unadjusted Earnings past Labor Strength, Physician-Specific Job, and Work-Family Characteristics

Overall pediatrician-reported mean annual income in 2016 was $189 804. For women, the unadjusted mean was $176 738 (median = $170 000, minimum = $9000, maximum = $750 000, and skewness = 1.76). For men, the mean was $226 133 (median = $210 000, minimum = $80 000, maximum = $700 000, and skewness = 1.83).

Across characteristics, female pediatricians consistently reported lower mean earnings than male pediatricians (Tabular array two). For example, for those in full general pediatrics, women reported earning $171 047 and men reported $223 472. Also, a disproportionate number of women were in the lowest-paid group of several characteristics. For example, 418 of 734 women (56.9%) and 89 of 264 men (33.7%) reported working in general pediatrics, and 271 of 727 women (37.3%) and 35 of 261 men (13.4%) reported working <40 hours per calendar week.

TABLE 2

Pediatrician Yearly Unadjusted Earnings past Gender and Labor Strength, Physician-Specific Job, and Work-Family Characteristics

Characteristics	Mean Unadjusted Yearly Earnings
	Female, Mean $ (n)	Male, Mean $ (n)	All, Mean $ (SD)
Total, Due north = 998c	176 738 (734)	226 133 (264)	189 804 (80 265)
Labor force
Years since residency training completionb,c
five–9	172 172 (385)	206 581 (124)	180 555 (sixty 872)
10–15	181 744 (349)	243 450 (140)	199 432 (95 513)
Race and ethnicity a , c
Asian, non-Hispanic	162 108 (129)	228 125 (40)	177 734 (68 849)
Other (includes Hispanic, African American or black, other)	169 250 (128)	192 643 (28)	173 449 (66 182)
White, not-Hispanic	182 703 (477)	230 510 (196)	196 626 (84 956)
Medical school location
International	167 500 (79)	220 636 (33)	183 156 (72 285)
The states	177 746 (653)	226 918 (231)	190 595 (81 282)
Region of state
Northeast	167 433 (146)	214 783 (46)	178 777 (77 891)
Due west	174 283 (189)	225 213 (61)	186 710 (76 060)
Midwest	180 772 (162)	228 985 (66)	194 728 (82 907)
South	183 021 (232)	230 418 (91)	196 374 (82 243)
Hours worked per wk a , c
<30	107 706 (126)	173 333 (iii)	109 233 (52 530)
30–39	185 172 (145)	226 406 (32)	192 627 (90 159)
40–49	189 633 (211)	231 243 (70)	199 998 (69 819)
50–59	193 630 (150)	215 899 (89)	201 922 (64 124)
≥60	200 868 (95)	239 433 (67)	216 818 (88 128)
Specialty a , b , c
General pediatrics	171 047 (418)	223 472 (89)	180 250 (83 815)
Hospitalist	173 651 (86)	204 031 (32)	181 890 (55 195)
Subspecialty, smaller	163 669 (108)	178 542 (48)	168 245 (68 913)
Subspecialty, larger	209 982 (122)	260 116 (95)	231 930 (76 520)
Physician-specific job
Years at current job a , b , c
<three	164 838 (187)	201 500 (70)	174 824 (62 826)
3–v	164 616 (229)	231 791 (86)	182 956 (76 649)
>five	192 465 (318)	237 593 (108)	203 905 (89 584)
No. patients per wk a , b , c
<60	159 339 (357)	211 550 (140)	174 046 (70 985)
60 or more	194 432 (367)	243 225 (120)	206 455 (85 721)
Owner a , b , c
No, employee or contractor	168 393 (610)	220 479 (217)	182 060 (71 005)
Yes	219 857 (122)	252 234 (47)	228 861 (107 192)
Work area
Inner city	164 447 (152)	213 197 (66)	179 206 (63 259)
Suburban	178 790 (296)	240 654 (78)	191 692 (90 622)
Urban	177 130 (221)	226 520 (100)	192 516 (71 757)
Rural	194 259 (58)	210 250 (20)	198 359 (91 299)
Academic work setting a , b , c
Yes	169 087 (257)	207 255 (106)	180 233 (65 468)
No	181 034 (475)	238 797 (158)	195 452 (87 261)
Compensation method a , b , c
Fixed salary, share of practice billings and/or workload, or shift or hourly	165 569 (525)	216 216 (185)	178 765 (73 659)
Mix of salary and billings or other measures of operation	204 913 (206)	249 354 (79)	217 232 (89 376)
Piece of work-family unit
Married or partnered
No	171 701 (93)	210 185 (27)	180 360 (65 274)
Yes	177 649 (638)	228 123 (235)	191 236 (82 208)
No. children b , c
0	178 941 (124)	201 947 (38)	184 338 (59 151)
1	169 043 (121)	191 029 (35)	173 976 (62 343)
ii	180 974 (316)	232 353 (102)	193 512 (80 750)
≥3	172 801 (173)	244 701 (87)	196 860 (97 760)
Choices in income for own children a , c
Yep	158 493 (336)	210 527 (74)	167 884 (77 331)
No	192 215 (396)	232 211 (190)	205 183 (78 837)
Choices in income for piece of work surroundings a , b , c
Yes	162 670 (261)	206 767 (90)	173 977 (73 591)
No	184 378 (469)	235 930 (172)	198 211 (82 633)
Work reduction: reported working part-time and/or not working on ≥one surveys over the final v y a , c
Yes	149 711 (325)	207 129 (31)	154 711 (75 003)
No	198 214 (409)	228 661 (233)	209 264 (76 431)

Characteristics	Hateful Unadjusted Yearly Earnings
	Female, Mean $ (n)	Male, Hateful $ (northward)	All, Mean $ (SD)
Total, North = 998c	176 738 (734)	226 133 (264)	189 804 (eighty 265)
Labor force
Years since residency training completionb,c
v–9	172 172 (385)	206 581 (124)	180 555 (threescore 872)
10–fifteen	181 744 (349)	243 450 (140)	199 432 (95 513)
Race and ethnicity a , c
Asian, non-Hispanic	162 108 (129)	228 125 (40)	177 734 (68 849)
Other (includes Hispanic, African American or blackness, other)	169 250 (128)	192 643 (28)	173 449 (66 182)
White, non-Hispanic	182 703 (477)	230 510 (196)	196 626 (84 956)
Medical schoolhouse location
International	167 500 (79)	220 636 (33)	183 156 (72 285)
U.s.	177 746 (653)	226 918 (231)	190 595 (81 282)
Region of country
Northeast	167 433 (146)	214 783 (46)	178 777 (77 891)
Westward	174 283 (189)	225 213 (61)	186 710 (76 060)
Midwest	180 772 (162)	228 985 (66)	194 728 (82 907)
South	183 021 (232)	230 418 (91)	196 374 (82 243)
Hours worked per wk a , c
<30	107 706 (126)	173 333 (3)	109 233 (52 530)
30–39	185 172 (145)	226 406 (32)	192 627 (ninety 159)
40–49	189 633 (211)	231 243 (70)	199 998 (69 819)
fifty–59	193 630 (150)	215 899 (89)	201 922 (64 124)
≥sixty	200 868 (95)	239 433 (67)	216 818 (88 128)
Specialty a , b , c
General pediatrics	171 047 (418)	223 472 (89)	180 250 (83 815)
Hospitalist	173 651 (86)	204 031 (32)	181 890 (55 195)
Subspecialty, smaller	163 669 (108)	178 542 (48)	168 245 (68 913)
Subspecialty, larger	209 982 (122)	260 116 (95)	231 930 (76 520)
Physician-specific job
Years at electric current job a , b , c
<3	164 838 (187)	201 500 (seventy)	174 824 (62 826)
iii–five	164 616 (229)	231 791 (86)	182 956 (76 649)
>5	192 465 (318)	237 593 (108)	203 905 (89 584)
No. patients per wk a , b , c
<lx	159 339 (357)	211 550 (140)	174 046 (70 985)
60 or more	194 432 (367)	243 225 (120)	206 455 (85 721)
Owner a , b , c
No, employee or contractor	168 393 (610)	220 479 (217)	182 060 (71 005)
Yes	219 857 (122)	252 234 (47)	228 861 (107 192)
Piece of work area
Inner urban center	164 447 (152)	213 197 (66)	179 206 (63 259)
Suburban	178 790 (296)	240 654 (78)	191 692 (90 622)
Urban	177 130 (221)	226 520 (100)	192 516 (71 757)
Rural	194 259 (58)	210 250 (20)	198 359 (91 299)
Academic work setting a , b , c
Yes	169 087 (257)	207 255 (106)	180 233 (65 468)
No	181 034 (475)	238 797 (158)	195 452 (87 261)
Bounty method a , b , c
Stock-still salary, share of practice billings and/or workload, or shift or hourly	165 569 (525)	216 216 (185)	178 765 (73 659)
Mix of salary and billings or other measures of performance	204 913 (206)	249 354 (79)	217 232 (89 376)
Work-family
Married or partnered
No	171 701 (93)	210 185 (27)	180 360 (65 274)
Aye	177 649 (638)	228 123 (235)	191 236 (82 208)
No. children b , c
0	178 941 (124)	201 947 (38)	184 338 (59 151)
1	169 043 (121)	191 029 (35)	173 976 (62 343)
2	180 974 (316)	232 353 (102)	193 512 (80 750)
≥three	172 801 (173)	244 701 (87)	196 860 (97 760)
Choices in income for ain children a , c
Yes	158 493 (336)	210 527 (74)	167 884 (77 331)
No	192 215 (396)	232 211 (190)	205 183 (78 837)
Choices in income for work environment a , b , c
Yes	162 670 (261)	206 767 (xc)	173 977 (73 591)
No	184 378 (469)	235 930 (172)	198 211 (82 633)
Work reduction: reported working part-fourth dimension and/or non working on ≥1 surveys over the last five y a , c
Yes	149 711 (325)	207 129 (31)	154 711 (75 003)
No	198 214 (409)	228 661 (233)	209 264 (76 431)

a

I-way assay of variance, P < .05 for female (examination examined bivariate associations for annual earnings and characteristics separately for female and male pediatricians).

b

One-fashion assay of variance, P < .05 for male person.

c

One-way analysis of variance, P < .05 for all (both female person and male person).

Overall earnings were associated with many of the characteristics (Table 2). Amongst labor force characteristics, pediatricians in the larger subspecialty group (eg, neonatology, cardiology, critical care, emergency medicine, gastroenterology, and hematology-oncology) and those working ≥lx hours reported the highest incomes, reporting overall mean earnings of $231 930 and $216 818, respectively. Amidst physician-specific characteristics, pediatricians who reported more years at their current job, more than patients per week, being an owner, working in a nonacademic piece of work setting, and existence paid past a mix of salary and billings or performance measures reported higher earnings. For case, the overall mean income of owners was $228 861 and employees was $182 060, P < .001. Amid piece of work-family unit characteristics, large differences in mean unadjusted earnings were found. For example, the overall mean for pediatricians who reported making choices in their earned income for their children was significantly lower than those non reporting such choices ($167 884 vs $205 183, P < .001).

Independent Result of Gender on Log of Pediatrician Earnings: Unadjusted and Adjusted Earnings

In Effigy 1, we nowadays the male gender coefficient and R ^two for four models using the log of annual earnings. Without any adjustment, male pediatricians earned $51 319 more than female pediatricians, P < .001 (model one). Adjusting for labor strength characteristics, men earned $25 566 more women, P < .001 (model 2). Adjusting for both labor strength and dr.-specific job characteristics, men earned $14 240 more than women, P < .001 (model 3). Finally, when adjusting for labor forcefulness, physician-specific job, and work-family characteristics, men earned $7997 more than women, P < .05 (model four). The model R ² increased across the 4 models, from 0.08 in the unadjusted model 1 to 0.48 in model four. We used likelihood ratio tests to appraise whether each subsequent model led to a significant increase in model fit; across models, each increment in explained earnings variation was statistically significant (P < .001). The female-to-male earnings ratio increased beyond models, from 0.76 in model 1, to 0.87 in model 2, 0.91 in model three, and 0.94 in model four. The total model results are presented in Supplemental Table 4.

Figure 1

Female person-male pediatrician almanac earnings gap, unadjusted and adjusted by labor force, physician-specific job, and work-family characteristics determined by using ordinary least squares regression. Gender coefficient ($): male person (ref = female); confined correspond 95% confidence intervals. Dollar amounts are log of almanac earnings (95% confidence interval) converted back to original scale ($). For total model results, see Supplemental Table 4. Labor force characteristics are as follows: gender, years since residency graduation, race and ethnicity, medical school location, region of country, number of piece of work hours per calendar week, and specialty. Physician-specific job characteristics are every bit follows: years in current position, number of patients seen in a typical calendar week, owner, work expanse, academic setting, and bounty method. Work-family characteristics are as follows: marital status, parental status, choices made for ain children, choices fabricated for work environment, and work reduction over past five survey years.

FIGURE i

Female-male pediatrician annual earnings gap, unadjusted and adjusted by labor forcefulness, physician-specific chore, and work-family characteristics determined by using ordinary least squares regression. Gender coefficient ($): male (ref = female); confined stand for 95% confidence intervals. Dollar amounts are log of annual earnings (95% confidence interval) converted back to original scale ($). For full model results, see Supplemental Table 4. Labor strength characteristics are as follows: gender, years since residency graduation, race and ethnicity, medical school location, region of country, number of work hours per week, and specialty. Md-specific task characteristics are as follows: years in current position, number of patients seen in a typical calendar week, owner, work surface area, academic setting, and compensation method. Piece of work-family characteristics are equally follows: marital condition, parental status, choices made for own children, choices fabricated for work environment, and piece of work reduction over by 5 survey years.

Shut modal

Discussion

Nosotros analyzed cocky-reported 2016 annual earnings amidst early on- to midcareer pediatricians and institute women earn less than men. Before any aligning, female pediatricians earn 76% of what male person pediatricians earn, or ∼$51 000 less. Adjusting for common labor force characteristics, such as demographics, experience, work hours, and specialty, female pediatricians earn 87% of what male pediatricians earn, or ∼$26 000 less. Adjusting for both labor force and physician-specific job characteristics, female pediatricians earn 91% of male person pediatrician earnings, or ∼$fourteen 000 less. Finally, adjusting for a comprehensive prepare of labor force, task-specific, and work-family variables, female pediatricians earn 94% of male pediatrician earnings, or ∼$8000 less.

Our study is comparable to gender differences establish in other studies of U.s.a. workers. In our unadjusted model, we institute the female-to-male earnings ratio was 0.76. Among full-time Us workers, the female-to-male person unadjusted almanac earnings ratio was 0.81 in 2016.⁴⁷ A US government written report by the Articulation Economical Commission summarized the persistent gender earnings gap across diverse occupations and education levels, with the largest pay gap among those with professional degrees and worse for mothers and function-time workers.⁴⁸

Our findings compare with studies of other physicians in which researchers found large unadjusted gender differences in income and smaller differences when adjustments are made, ranging from women earning viii% to 38% less than men.^{9,13,17–19,49–51} Because we controlled for labor strength, job-specific, and work-family unit variables in our study, the findings support previous reports in which authors suggested earnings gaps may be related to mothers who might prioritize job flexibility¹⁹ and that periods of part-time work and not working are related to earnings.¹²

The role of employment negotiations and bigotry in the earnings gap has been discussed.⁴⁴ Women and men may make different choices, negotiate differently, or experience unlike pressure from themselves or others regarding their own children and families. Women, including physicians, spend more time on household and kid care responsibilities than men.^46,52,53 They may accept lower salaries for nonmonetary benefits, such as fewer work hours,⁵⁴ flexibility of work hours, and location or community of work identify.⁴⁴ Our study is unique considering our focus is on one specialty, and nosotros adjust for a comprehensive set up of work-life characteristics that other researchers have suggested are important.^{12,eighteen–xx} Besides labor force variables (eg, work hours and specialty), some of the largest differences in earnings were among the work-family characteristics. Still, even when controlling for these characteristics, we found that early- to midcareer women in pediatrics earned $8000 less than men.

The gender pay gap is complex and multifaceted. Economists have discussed the upshot extensively and propose explanations such equally education, occupation choice, experience, work place flexibility and hours, negotiation skills, competitiveness, parenting, and fourth dimension away from piece of work.^55,56 They propose that, in part, women earn less because of long work hours and inflexible conflict with dwelling house responsibilities.⁵⁵ In a study of chief of business administration graduates, information technology was found that women and men had similar income and work hours at the start of their careers, but over time, both income and work hours increasingly diverged.⁵⁵ Researchers also reported that work interruptions and differences in hours worked explained much of the gender gap in earnings and having children was related to more work interruptions, fewer hours worked, and less income for women only not men. Economist Claudia Goldin suggests that to obtain convergence of pay, changes to the structure and renumeration of jobs are needed (eg, more than-flexible work schedules without reduction in opportunities and pay).⁵⁷

U.s.a. academic medical institutions are working toward policies to reach pay equity.⁵⁸ In a study of an academic pediatrics department, gender salary inequalities were found and an intervention strategy was designed that focused on several areas, including bacon corrections and annual monitoring of salaries.^xv Postintervention, gender bacon differences were no longer pregnant. Their intervention might exist helpful to other doc employers.

Our study has strengths, including a national sample, focus on a specialty with a bulk of women, and examination of a comprehensive set of physician characteristics. Limitations include that data are self-reported by early on- and midcareer pediatricians and many different pediatric subspecialties were combined because of bereft numbers to examine separately. The generalizability to other specialties or career stages and whether our findings differ from studies of specialties with lower portions of women is unclear. The difference in earnings might exist more substantial as pediatricians progress through their careers. Although the response among PLACES participants who completed the study surveys was loftier, the initial project sign-upward rate was lower, at ∼40%, and women, United states medical school graduates, and AAP members were more than probable to sign upwardly. We could non adjust for all possible characteristics, so it remains unclear whether information technology is merely gender that explains the remaining income gap between men and women. Because of small numbers of Hispanic and African American pediatricians in our sample, we combined these pediatricians with those who specified "other race" and were not sufficiently powered to examine differences in earnings for specific groups. Finally, the data are cantankerous-exclusive and therefore do not explore how pediatrician earnings and the gender gap progress over time, which will be possible with hereafter PLACES data.

Conclusions

In this study, nosotros constitute gender differences in annual earnings among early on- and midcareer pediatricians, with women earning less than men. This difference persists after adjustment for of import labor force, dr.-specific, and work-life characteristics. With more than female person physicians in the workforce, the findings can be important to understanding overall physician earnings. In future piece of work, researchers should use longitudinal analysis and further explore periods of office-time work, family unit obligations, and work flexibility.

Acknowledgments

We thank the pediatricians participating in PLACES who are all giving generously of their time to make this project possible. This research was supported past the AAP.

Ms Frintner conceptualized and designed the written report, conducted the analyses, coordinated and supervised data collection, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Sisk assisted with the formulation and design of the study, reviewed and helped interpret the analyses, and reviewed and revised the manuscript; Drs Olson, Byrne, Freed, and Starmer assisted with the conception and blueprint of the study and critically reviewed the manuscript; and all authors approved the final manuscript as submitted.

The research presented in this article is that of the authors and does non reverberate the official policy of the AAP.

FUNDING: This study was supported by the American Academy of Pediatrics.

• AAP

  American Academy of Pediatrics

• PLACES

  Pediatrician Life and Career Feel Study

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors accept indicated they accept no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors accept indicated they have no financial relationships relevant to this commodity to disembalm.

Copyright © 2019 by the American University of Pediatrics

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